Metadata-Version: 2.1
Name: nessai-models
Version: 0.1.0
Summary: Models for nessai
Home-page: https://github.com/mj-will/nessai-models
Author: Michael J. Williams
Author-email: m.williams.4@research.gla.ac.uk
License: MIT
Keywords: nested sampling,normalising flows,machine learning,nessai
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.9)
Requires-Dist: scipy (>=0.16)
Requires-Dist: nessai (>=0.6.0)
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: pytest (>=6.0) ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: pytest-integration ; extra == 'dev'

# nessai-models

Models for use with the nested sampling package [`nessai`](https://github.com/mj-will/nessai).

## Included models

* n-dimensional unit Gaussian
* n-dimensional HalfGaussian
* n-dimensional Rosenbrock
* n-dimensional mixture of Gaussians
* Gaussian mixture using data to based on [this example](https://github.com/johnveitch/cpnest/blob/master/examples/gaussianmixture.py) from `cpnest`
* n-dimensional Egg Box based on the version in [Feroz et al. 2008](https://arxiv.org/abs/0809.3437)
* n-dimensional Pyramid-like model

## Requirements

`nessai_models` requires:
* `numpy`
* `scipy`
* `nessai>=0.6.0`

## Installation

> We recommend following the [installation instructions for `nessai`](https://github.com/mj-will/nessai#installation) and then installing `nessai_models` since it shares all of its dependencies with `nessai`.

`nessai_models` can be install from PyPI using

```console
pip install nessai-models
```

## Example usage

Below is an example of using `nessai_models` so configure a 4-dimensional Gaussian and then sample it using `nessai`.

```python
from nessai import FlowSampler
from nessai_models import Gaussian

model = Gaussian(4)
fs = FlowSampler(model, output='example/')
fs.run()
```
